Summary of Automated Coastline Extraction Using Edge Detection Algorithms, by Conor O’sullivan et al.
Automated Coastline Extraction Using Edge Detection Algorithms
by Conor O’Sullivan, Seamus Coveney, Xavier Monteys, Soumyabrata Dev
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper compares four edge detection algorithms (Canny, Sobel, Scharr, and Prewitt) for extracting coastlines from satellite images. The authors evaluate their performance visually and using metrics such as structural similarity index measurement (SSIM). Canny edges were found to be closest to the reference edges with an average SSIM of 0.8. However, the algorithm struggled to distinguish between noisy edges and coastline edges. Additionally, preprocessing techniques like histogram equalization and Gaussian blur can improve edge detection effectiveness by up to 1.5 and 1.6 times, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different algorithms for finding edges in satellite images. These edges help define coastlines. The authors tested four algorithms: Canny, Sobel, Scharr, and Prewitt. They looked at how well the algorithms worked by comparing them to a reference image. One algorithm, Canny, did a good job of finding edges with an average score of 0.8. However, it had trouble telling noisy edges from coastline edges. The authors also showed that making some changes to the images before using the algorithms can make them work better. |
